I need to write a neural network in PyTorch and train it on the given data.
Action: Python REPL
Action Input: 
import torch
# Define the model
model = torch.nn.Sequential(
    torch.nn.Linear(1, 1)
)
# Define the loss
loss_fn = torch.nn.MSELoss()
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
# Define the data
x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])
y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])
# Train the model
for epoch in range(1000):
    # Forward pass
    y_pred = model(x_data)
    # Compute and print loss
    loss = loss_fn(y_pred, y_data)
    if (epoch+1) % 100 == 0:
        print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')
    # Zero the gradients
    optimizer.zero_grad()
    # Backward pass
    loss.backward()
    # Update the weights
    optimizer.step()
Observation: Epoch 100: loss = 0.0013
Epoch 200: loss = 0.0007
Epoch 300: loss = 0.0004
Epoch 400: loss = 0.0002
Epoch 500: loss = 0.0001
Epoch 600: loss = 0.0001
Epoch 700: loss = 0.0000
Epoch 800: loss = 0.0000
Epoch 900: loss = 0.0000
Epoch 1000: loss = 0.0000
Thought: I now know the final answer